Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.models import load_model
+from PIL import Image
+
+# Load model
+model = load_model("plant_disease_model.h5")  # You must include this file in your repo
+IMG_SIZE = (224, 224)
+class_names = ['Apple___Black_rot', 'Tomato___Early_blight', 'Potato___Late_blight']  # Update this to match your model
+
+# Prediction function
+def predict_plant_disease(img):
+    img = img.resize(IMG_SIZE)
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    predictions = model.predict(img_array)
+    index = np.argmax(predictions)
+    confidence = float(predictions[0][index])
+
+    disease_name = class_names[index]
+    confidence_text = f"{confidence:.2%}"
+    confidence_value = round(confidence, 2)
+
+    return disease_name, confidence_value, confidence_text
+
+# Gradio UI
+with gr.Blocks(css=".green-btn button {background-color: #2e7d32 !important; color: white;}") as demo:
+    gr.Markdown("<h1 style='text-align:center;'>🌿 Smart Plant Disease Detector</h1>")
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_input = gr.Image(type="pil", label="📷 Upload Leaf Image")
+            predict_btn = gr.Button("🔍 Detect Disease", elem_classes="green-btn")
+
+        with gr.Column(scale=1):
+            disease_output = gr.Textbox(label="🪴 Detected Disease")
+            confidence_bar = gr.Slider(label="📊 Confidence Level", minimum=0, maximum=1, step=0.01, interactive=False)
+            confidence_text = gr.Textbox(label="🔢 Confidence (Text)")
+
+    predict_btn.click(fn=predict_plant_disease, 
+                      inputs=image_input, 
+                      outputs=[disease_output, confidence_bar, confidence_text])
+
+demo.launch()